Learning user embeddings (representations) has become an increasingly popular method of improving recommendations.
Such embeddings have been adopted to power ranking and candidate generation in industry, and are used to power personalized recommendations across YouTube [1]}, Google Play [2]}, Airbnb search [3]}, JD.com search [4]}, Alibaba [5]}, [6]}, and more.
In addition to work on learning personalized embeddings, there is a body of work focused on directly building ranking models using sequential information [7]}, [8]}, [6]}, [10]}, enabling personalization of recommendations based on a user's recent engagement.
